Suchergebnisse

Purpose
Fierce competition drives software vendors to rely on Software-as-a-Service (SaaS) strategies and to continuously match new releases with customers' needs and competitors' moves. Such recurrent release practices pose specific challenges for software vendors which shape how they service customers. To address these challenges, this paper aims to apply service science to innovate strategies for SaaS release management.


Design/methodology/approach
Based on action research methodology, the authors collaborated closely with Software Inc., an alias for a large multinational software provider, to apply service-dominant logic systematically, to analyze and improve its SaaS release management process and to support ongoing value co-creation with its customers.


Findings
The authors provide a detailed account of how Software Inc. improved its SaaS release management practices; they extend current understanding of service innovation dynamics in SaaS environments and offer a model of value co-creation in SaaS release management grounded in the findings from Software Inc.


Research limitations/implications
The research draws on a single case study with particular characteristics. Still, it allows for analytical generalizations with both theoretical and practical implications for how SaaS managers can improve recurrent release practices based on foundational service-dominant logic principles.


Practical implications
The authors suggest that SaaS managers concentrate on knowledge-sharing with customers, ensure continuous communication among teams supporting the service, re-organize release management to enhance the value co-creation process, use technology to improve customer service experiences and use service mapping to improve release management and service quality.


Originality/value
The authors bridge service-dominant logic principles and SaaS knowledge by demonstrating how service-dominant logic can be used to improve SaaS release practices and by offering conceptual and practical knowledge about value co-creation between customers and suppliers in SaaS contexts.

In the past decades, unmanned aerial vehicles (UAVs), also known as drones, have drawn more attention in the academic domain and exploration in the research fields of wireless sensor networks (WSNs). Moreover, applications of drones aid operations related to military support, agriculture industry, and smart Internet-of-Things (IoT). Currently, the use of drone based IoT, also known as Internet-of-Drones (IoD), and their design challenges and techniques are being probed by researchers around the globe. The placement of drones (nodes) is an important consideration in a IoD environment and is closely related to the properties of IoT. Given a base station (BS), sensor nodes (SNs) and IoT devices are designed to capture the signals transmitted by the BS and make use of internet connectivity in a manner to facilitate users. Mutual benefit can be achieved by integrating drones into IoT. The drone based cluster models are not free from challenges. Routing protocols have to be substantiated by key algorithms. Drones are designed to be specific to applications, but the underlying principles are the same. Optimization algorithms are the gateway to better accuracy, performance, and reliability. This article discusses some of these optimization algorithms, include genetic algorithm (GA), bee optimization algorithm, and Chicken Swarm Optimization Clustering Algorithm (CSOCA). Finally, the routing schemes, protocols, and challenges in the context of IoD are discussed.

In the past decades, unmanned aerial vehicles (UAVs), also known as drones, have drawn more attention in the academic domain and exploration in the research fields of wireless sensor networks (WSNs). Moreover, applications of drones aid operations related to military support, agriculture industry, and smart Internet-of-Things (IoT). Currently, the use of drone based IoT, also known as Internet-of-Drones (IoD), and their design challenges and techniques are being probed by researchers around the globe. The placement of drones (nodes) is an important consideration in a IoD environment and is closely related to the properties of IoT. Given a base station (BS), sensor nodes (SNs) and IoT devices are designed to capture the signals transmitted by the BS and make use of internet connectivity in a manner to facilitate users. Mutual benefit can be achieved by integrating drones into IoT. The drone based cluster models are not free from challenges. Routing protocols have to be substantiated by key algorithms. Drones are designed to be specific to applications, but the underlying principles are the same. Optimization algorithms are the gateway to better accuracy, performance, and reliability. This article discusses some of these optimization algorithms, include genetic algorithm (GA), bee optimization algorithm, and Chicken Swarm Optimization Clustering Algorithm (CSOCA). Finally, the routing schemes, protocols, and challenges in the context of IoD are discussed.